<group>
<ul class='breadcrumb'><li><a href='%pathto:mdoc;'>Index</a></li><li><a href='%pathto:sift.vl_siftdescriptor;'>Prev</a></li><li><a href='%pathto:sift.vl_ubcread;'>Next</a></li></ul><div class="documentation"><p>
MATCHES = <a href="%pathto:sift.vl_ubcmatch;">VL_UBCMATCH</a>(DESCR1, DESCR2) matches the two sets of SIFT
descriptors DESCR1 and DESCR2.
</p><p>
[MATCHES,SCORES] = <a href="%pathto:sift.vl_ubcmatch;">VL_UBCMATCH</a>(DESCR1, DESCR2) retuns the matches and
also the squared Euclidean distance between the matches.
</p><p>
The function uses the algorithm suggested by D. Lowe [1] to reject
matches that are too ambiguous.
</p><p>
<a href="%pathto:sift.vl_ubcmatch;">VL_UBCMATCH</a>(DESCR1, DESCR2, THRESH) uses the specified threshold
THRESH. A descriptor D1 is matched to a descriptor D2 only if the
distance d(D1,D2) multiplied by THRESH is not greater than the
distance of D1 to all other descriptors. The default value of
THRESH is 1.5.
</p><p>
The storage class of the descriptors can be either DOUBLE, FLOAT,
INT8 or UINT8. Usually integer classes are faster.
</p><dl><dt>
REFERENCES
</dt><dd><p>
[1] D. G. Lowe, Distinctive image features from scale-invariant
keypoints. IJCV, vol. 2, no. 60, pp. 91-110, 2004.
</p></dd></dl><p>
See also: <a href="%pathto:vl_help;">VL_HELP</a>(), <a href="%pathto:sift.vl_sift;">VL_SIFT</a>().
</p></div></group>
